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Record W4386099018 · doi:10.3390/membranes13090750

A Comparative Study of the Self-Cleaning and Filtration Performance of Suspension Plasma-Sprayed TiO2 Ultrafiltration and Microfiltration Membranes

2023· article· en· W4386099018 on OpenAlexafffund
Elnaz Alebrahim, Christian Moreau

Bibliographic record

VenueMembranes · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsMicrofiltrationMembraneUltrafiltration (renal)Filtration (mathematics)Materials scienceFoulingSuspension (topology)Titanium dioxideChemical engineeringChromatographyMembrane foulingChemistryComposite material

Abstract

fetched live from OpenAlex

This study investigated the performance of photocatalytic titanium dioxide microfiltration membranes with an average pore size of approximately 180 nm and ultrafiltration membranes with an average pore size of around 40 nm fabricated with the suspension plasma spray process. The membranes were evaluated for their filtration performance using SiO2 particles of different sizes and polyethylene oxide with molecular weights of 20 kDa to 1000 kDa, and the fouling parameters were characterized. The rejection rate was enhanced by increasing the thickness of the membranes. This effect was more pronounced with the ultrafiltration membranes. The rejection rate of the ultrafiltration membrane was improved significantly after filling the larger pores on the surface with agglomerates of titanium dioxide nanoparticles. The self-cleaning performance of the membranes was assessed under visible light. Both ultrafiltration and microfiltration membranes showed a flux recovery under visible light illumination due to the photocatalytic activity of titanium dioxide. The membranes also show a flux recovery of more than 90%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.246
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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